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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

A coverage and slicing dependencies analysis for seeking software security defects.

Hui He¹, Dongyan Zhang², Min Liu¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

Thescientificworldjournal

|July 2, 2014

Summary

This summary is machine-generated.

This study introduces a new automated fault localization method that precisely identifies software security flaws at the sentence level. This approach enhances software assurance by improving vulnerability detection accuracy and effectiveness.

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Area of Science:

Computer Science
Software Engineering
Cybersecurity

Background:

Software security defects significantly degrade software quality and reliability, posing risks to system operations.
Increasing software scale complicates the detection of vulnerabilities, which can lead to substantial losses if exploited.
Software Assurance research addresses these issues, with automated fault localization being a key component.

Purpose of the Study:

To develop a novel automated fault localization method that overcomes limitations of existing techniques.
To enhance the accuracy and precision of identifying software security flaws.
To improve the overall effectiveness of automated vulnerability detection in software.

Main Methods:

Integration of Coverage Based Fault Localization (CBFL) and program slicing techniques.
Development of a Reverse Data Dependence Analysis Model analyzing program structure.
Refinement of the basic location unit to the single sentence level for enhanced precision.

Main Results:

The proposed method achieves lossless automation in fault localization.
The single-sentence location unit significantly improves localization accuracy.
Experimental results demonstrate superior effectiveness compared to existing methods.

Conclusions:

The Reverse Data Dependence Analysis Model offers a more accurate and effective approach to automated fault localization.
This method contributes to advancing Software Assurance by improving vulnerability detection capabilities.
The single-sentence granularity provides a finer level of detail for software defect analysis.